In this paper, we focus on the problem of category-level object pose
estimation, which is challenging due to the large intra-category shape
variation. 3D graph convolution (3D-GC) based methods have been widely used to
extract local geometric features, but they have limitations for complex shaped
objects and are sensitive to noise. Moreover, the scale and translation
invariant properties of 3D-GC restrict the perception of an object's size and
translation information. In this paper, we propose a simple network structure,
the HS-layer, which extends 3D-GC to extract hybrid scope latent features from
point cloud data for category-level object pose estimation tasks. The proposed
HS-layer: 1) is able to perceive local-global geometric structure and global
information, 2) is robust to noise, and 3) can encode size and translation
information. Our experiments show that the simple replacement of the 3D-GC
layer with the proposed HS-layer on the baseline method (GPV-Pose) achieves a
significant improvement, with the performance increased by 14.5% on 5d2cm
metric and 10.3% on IoU75. Our method outperforms the state-of-the-art methods
by a large margin (8.3% on 5d2cm, 6.9% on IoU75) on the REAL275 dataset and
runs in real-time (50 FPS).

本研究提出了一种名为 HS-layer 的简单网络结构，可以从点云数据中提取混合范围潜在特征，用于类别级对象姿态估计任务。使用 HS-layer 取代 GPV-Pose 基线方法中的 3D-GC 层，可以显著提高性能，在 5d2cm 指标上提高 14.5％，在 IoU75 上提高 10.3％。与现有方法相比，在 REAL275 数据集上的表现优异，较之提高 8.3％（5d2cm）和 6.9％（IoU75），且运行速度快（50 FPS） 。

HS-Pose: 混合范围特征提取用于类别级目标姿态估计

HS-Pose: Hybrid Scope Feature Extraction for Category-level Object Pose  Estimation

In this paper, we propose a novel 3D graph convolution based pipeline for
category-level 6D pose and size estimation from monocular RGB-D images. The
proposed method leverages an efficient 3D data augmentation and a novel
vector-based decoupled rotation representation. Specifically, we first design
an orientation-aware autoencoder with 3D graph convolution for latent feature
learning. The learned latent feature is insensitive to point shift and size
thanks to the shift and scale-invariance properties of the 3D graph
convolution. Then, to efficiently decode the rotation information from the
latent feature, we design a novel flexible vector-based decomposable rotation
representation that employs two decoders to complementarily access the rotation
information. The proposed rotation representation has two major advantages: 1)
decoupled characteristic that makes the rotation estimation easier; 2) flexible
length and rotated angle of the vectors allow us to find a more suitable vector
representation for specific pose estimation task. Finally, we propose a 3D
deformation mechanism to increase the generalization ability of the pipeline.
Extensive experiments show that the proposed pipeline achieves state-of-the-art
performance on category-level tasks. Further, the experiments demonstrate that
the proposed rotation representation is more suitable for the pose estimation
tasks than other rotation representations.

本文提出了一种新型的基于 3D 图卷积的流水线，用于从单目 RGB-D 图像中的分类级别 6D 姿态和尺寸估计。提出的方法利用了高效的 3D 数据增强和新型的基于向量的分解旋转表示。实验结果表明，该流水线在分类级别任务上具有最先进的性能。同时，实验证明，所提出的旋转表示比其他旋转表示更适合姿态估计任务。